def scatter_nd_ad_run(indices_shape, data_shape, output_shape, indices_dtype, dtype, attrs):
kernel_name = utils.gen_name_kernel("scatter_nd_ad", dtype, data_shape)
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(scatter_nd_ad, [output_shape, indices_shape, data_shape],
[dtype, indices_dtype, dtype], op_attrs=[output_shape], kernel_name=kernel_name,
attrs=attrs, tuning=t)
if t:
data_input, expect, head_input, indicies_input, output = gen_data(data_shape, dtype, indices_dtype,
indices_shape, output_shape)
return mod, expect, (head_input, indicies_input, data_input, output)
else:
return mod
else:
data_input, expect, head_input, indicies_input, output = gen_data(data_shape, dtype, indices_dtype,
indices_shape, output_shape)
mod = utils.op_build_test(scatter_nd_ad, [output_shape, indices_shape, data_shape],
[dtype, indices_dtype, dtype], op_attrs=[output_shape], kernel_name=kernel_name,
attrs=attrs)
output = utils.mod_launch(mod, (head_input, indicies_input, data_input, output), expect=expect)
return (head_input, indicies_input, data_input), output, expect, compare_tensor(output, expect, rtol=5e-03,
equal_nan=True)
def focalloss_ad_run2(shape, dtype, attrs):
logits_pld = akg.tvm.placeholder(shape, dtype=dtype, name='logits')
labels_pld = akg.tvm.placeholder(shape, dtype='int32', name='labels')
d_labels, d_logits, head = focalloss_ad.focalloss_ad(
labels_pld, logits_pld)
print("autodiff d_logits:\n", akg.tvm.PrintTensorRecursively(d_logits))
print("autodiff d_labels:\n", akg.tvm.PrintTensorRecursively(d_labels))
# build autodiff kernels
io = [labels_pld, logits_pld, head, d_labels, d_logits]
s = akg.tvm.create_schedule([e.op for e in io])
kernel_name = utils.gen_name_kernel("focalloss_ad", dtype, (
shape[0],
shape[1],
))
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s,
io,
"cce",
name=kernel_name,
attrs=attrs,
polyhedral=True)
labels_np = RANGEFILL((batchsize, ))
logits_np = RANGEFILL((batchsize, ), 2)
head_np = RANGEFILL((batchsize, ), 2)
output = np.full(expect.shape, np.nan, dtype)
output = utils.mod_launch(mod, (labels_np, logits_np, head_np, output),
expect=output)
expect = output # hack
return (input_np, head_np), output, expect, compare_tensor(output,
expect,
atol=0.1)
def upsampling_run(in_shape, out_shape, dtype, kernel_name, attrs):
kernel_name = utils.gen_name_kernel(kernel_name, dtype, in_shape)
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(upsampling.upsampling,
input_shapes=[in_shape],
input_types=[dtype],
op_attrs=[out_shape],
kernel_name=kernel_name,
attrs=attrs,
tuning=t)
if t:
expect, input, output = gen_data(dtype, in_shape, out_shape)
return mod, expect, (input, output)
else:
return mod
else:
# Create op
mod = utils.op_build_test(upsampling.upsampling,
input_shapes=[in_shape],
input_types=[dtype],
op_attrs=[out_shape],
kernel_name=kernel_name,
attrs=attrs)
expect, input, output = gen_data(dtype, in_shape, out_shape)
output = utils.mod_launch(mod, (input, output), expect=expect)
return input, output, expect, compare_tensor(output,
expect,
atol=5e-01,
rtol=5e-03,
equal_nan=True)
def invert_permutation_run(shape, dtype, attrs):
# check shapes
vc_util.check_shape(shape)
if not (dtype.lower() in "int32"):
raise RuntimeError(
"indices_dtype only support int32 while dtype is %s" % dtype)
A = akg.tvm.placeholder(shape, dtype, name="A")
op = invert_permutation.invert_permutation(A)
s = akg.tvm.create_schedule(op.op)
kernel_name = utils.gen_name_kernel("invert_permutation", dtype, shape)
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s, [A, op],
"cce",
name=kernel_name,
attrs=attrs,
polyhedral=True)
input_data = np.random.permutation(np.arange(shape[0])).astype(np.int32)
expect = np.full([shape[0]], 0, np.int32)
for i, e in enumerate(input_data):
expect[e] = i
output = np.full([shape[0]], 0, np.int32)
output = utils.mod_launch(mod, (input_data, output), expect=expect)
return (input_data, ), output, expect, compare_tensor(output,
expect,
rtol=5e-03,
equal_nan=True)
def case_1(data_shape, dtype, kernel_name, attrs):
"""elemwise chain case 1"""
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.FLOAT16)
vc_util.check_shape_length_equal("data", data_shape, 2)
m, k = data_shape
A = akg.tvm.placeholder((m, k), name='A', dtype=dtype)
B = akg.tvm.placeholder((k, ), name='B', dtype=dtype)
C = akg.tvm.placeholder((m, k), name='C', dtype=dtype)
E = akg.tvm.compute((m, k),
lambda i, j: A[i, j] * (B[j] + C[i, j]),
name="E")
forward_s = akg.tvm.create_schedule(E.op)
op_vars = [A, B, C, E]
forward_low = akg.lower(forward_s,
op_vars,
simple_mode=True,
polyhedral=True)
kernel_name = utils.gen_name_kernel(kernel_name, dtype, data_shape)
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(forward_s,
op_vars,
"cce",
name="test",
attrs=attrs,
polyhedral=True)
source_code = mod.imported_modules[0].get_source()
return mod
def focalloss_grad_run(shape, dtype, label_dtype, gamma, attrs):
kernel_name = utils.gen_name_kernel("focalloss_grad", dtype, shape)
attrs["pragma_disable_whole_component"] = False
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(focalloss_grad.focal_loss_bwd,
input_shapes=[shape, shape, shape[:2]],
input_types=[dtype, label_dtype, dtype],
op_attrs=[gamma], kernel_name=kernel_name, attrs=attrs, tuning=t)
if t:
d_logits, expect_logits, grad_np, labels_np, logits_np = gen_grad_data(attrs, dtype, gamma, label_dtype,
shape)
return mod, expect_logits, (logits_np, labels_np, grad_np, d_logits)
else:
return mod
else:
mod = utils.op_build_test(focalloss_grad.focal_loss_bwd,
input_shapes=[shape, shape, shape[:2]],
input_types=[dtype, label_dtype, dtype],
op_attrs=[gamma], kernel_name=kernel_name, attrs=attrs)
d_logits, expect_logits, grad_np, labels_np, logits_np = gen_grad_data(attrs, dtype, gamma, label_dtype, shape)
d_logits = utils.mod_launch(mod, (logits_np, labels_np, grad_np, d_logits), expect=expect_logits)
result_logits = compare_tensor(d_logits, expect_logits, rtol=2e-2, atol=1e-4)
return (labels_np, logits_np, grad_np), d_logits, expect_logits, result_logits
def roipool(shape,
roibox,
pooled_shape,
dtype,
kernel_name="roipool_forward_output",
attrs=None):
check_list = ["float16"]
if not (dtype.lower() in check_list):
raise RuntimeError("tile_cce only support %s while dtype is %s" %
(",".join(check_list), dtype))
vc_util.check_shape(shape)
assert (len(shape) == 4)
assert (len(roibox) == 4)
assert (len(pooled_shape) == 2)
a_n, a_c, a_h, a_w = shape
roi_t, roi_b, roi_l, roi_r = roibox
assert (roi_t >= 0 and roi_t < roi_b and roi_b < a_h)
assert (roi_l >= 0 and roi_l < roi_r and roi_r < a_w)
a = akg.tvm.placeholder(shape, name="a", dtype=dtype)
Crop = akg.tvm.compute([a_n, a_c, roi_b - roi_t, roi_r - roi_l],
lambda n, c, h, w: a[n, c, roi_t + h, roi_l + w])
p_h, p_w = pooled_shape
win_h = (roi_b - roi_t) // p_h + (1 if (roi_b - roi_t) % p_h > 0 else 0)
win_w = (roi_r - roi_l) // p_w + (1 if (roi_r - roi_l) % p_w > 0 else 0)
assert p_h <= (roi_b - roi_t) and p_w <= (roi_r - roi_l)
Unpooled = akg.tvm.compute(
[a_n, a_c, p_h, p_w, win_h, win_w],
lambda n, c, h, w, wh, ww: akg.tvm.expr.Select(
akg.tvm.all(h * win_h + wh < roi_b - roi_t, w * win_w + ww < roi_r
- roi_l), Crop[n, c, h * win_h + wh, w * win_w + ww],
akg.tvm.const(0, a.dtype)))
rh = akg.tvm.reduce_axis((0, win_h))
rw = akg.tvm.reduce_axis((0, win_w))
output_shape = [a_n, a_c, p_h, p_w]
res = akg.tvm.compute(
output_shape,
lambda n, c, h, w: akg.tvm.max(Unpooled[n, c, h, w, rh, rw],
axis=[rh, rw]))
s = akg.tvm.create_schedule(res.op)
s[Crop].compute_inline()
s[Unpooled].compute_inline()
kernel_name = utils.gen_name_kernel(kernel_name, dtype, shape)
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s, [a, res],
"cce",
name=kernel_name,
attrs=attrs,
polyhedral=True)
return mod, output_shape
def encode_onehot_classes_run(gt_shape, anchor_shape, class_num, dtype,
kernel_name, attrs):
kernel_name = utils.gen_name_kernel(kernel_name, dtype, gt_shape)
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(encode_onehot_classes.encode_onehot_classes,
input_shapes=[gt_shape, anchor_shape],
input_types=[dtype, dtype],
op_attrs=[class_num],
kernel_name=kernel_name,
attrs=attrs,
tuning=t)
if t:
anchor_sample, expect, groundtruth_class, output = gen_data(
anchor_shape, class_num, dtype, gt_shape)
return mod, expect, (groundtruth_class, anchor_sample, output)
else:
return mod
else:
# Create op
mod = utils.op_build_test(encode_onehot_classes.encode_onehot_classes,
input_shapes=[gt_shape, anchor_shape],
input_types=[dtype, dtype],
op_attrs=[class_num],
kernel_name=kernel_name,
attrs=attrs)
anchor_sample, expect, groundtruth_class, output = gen_data(
anchor_shape, class_num, dtype, gt_shape)
output = utils.mod_launch(mod,
(groundtruth_class, anchor_sample, output),
expect=expect)
return (groundtruth_class,
anchor_sample), output, expect, compare_tensor(output,
expect,
atol=5e-01,
rtol=5e-03,
equal_nan=True)
def resize_bilinear_grad_run(resized_shape, original_shape, dtype, kernel_name, attrs):
kernel_name = utils.gen_name_kernel(kernel_name, dtype, resized_shape)
original_data = akg.tvm.placeholder(original_shape, dtype)
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(resize_bilinear_grad.resize_bilinear_grad,
[resized_shape], [dtype], op_attrs=[original_data],
kernel_name=kernel_name, attrs=attrs, tuning=t)
if t:
expect, input, output = gen_data(dtype, original_shape, resized_shape)
return mod, expect, (input, output)
else:
return mod
else:
mod = utils.op_build_test(resize_bilinear_grad.resize_bilinear_grad,
[resized_shape], [dtype], op_attrs=[original_data],
kernel_name=kernel_name, attrs=attrs)
expect, input, output = gen_data(dtype, original_shape, resized_shape)
# auto-tensor output
output = utils.mod_launch(mod, (input, output), expect=expect)
return input, output, expect, compare_tensor(output, expect, atol=5e-01, rtol=5e-03, equal_nan=True)
def sliceeven_run(shape, dtype, kernel_name="sliceeven_backward", attrs=None):
kernel_name = utils.gen_name_kernel(kernel_name, dtype, shape)
if 'tuning' in attrs.keys():
t = attrs.get("tuning", False)
kernel_name = attrs.get("kernel_name", False)
mod = utils.op_build_test(sliceeven.sliceeven, [shape], [dtype],
kernel_name=kernel_name,
attrs=attrs,
tuning=t)
if t:
expect, input_, output = gen_data(dtype, shape)
return mod, expect, (input_, output)
else:
return mod
else:
mod = utils.op_build_test(sliceeven.sliceeven, [shape], [dtype],
kernel_name=kernel_name,
attrs=attrs)
expect, input_, output = gen_data(dtype, shape)
output = utils.mod_launch(mod, (input_, output), expect=expect)
return (input_, ), output, expect, compare_tensor(output,
expect,
rtol=5e-03,
equal_nan=True)
